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1. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   https://doi.org/10.3390/fi14120354  

   Xu, Ronghua ; Chen, Yu ; Chen, Genshe ; Blasch, Erik ( December 2022 , Future Internet)

   The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. 

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2. Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework for Civilian Urban Air Mobility

   https://doi.org/10.3390/app13020755  

   Munir, Md. Shirajum ; Dipro, Sumit Howlader ; Hasan, Kamrul ; Islam, Tariqul ; Shetty, Sachin ( January 2023 , Applied Sciences)

   Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and detecting cyber jamming and spoofing attacks. Then, the developed framework analyzes the conditional dependencies based on the Pearson’s correlation coefficient among the control messages for finding the cause of potential attacks based on the outcome of the AI algorithm. This work considers the UAM attitude control scenario for determining jam and spoofing attacks as a use case to validate the proposed framework with a state-of-the-art UAV attack dataset. The experiment results show the efficacy of the proposed framework in terms of around 99.9% accuracy for jamming and spoofing detection with a decision tree, random forests, and KNN while efficiently finding the root cause of the attack. 

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3. Workshop development for New frontier of mechatronics for mobility, energy, and production engineering

   https://doi.org/10.18260/1-2--35709  

   Chen, J. C. ; Liao, G. Y. ; Lo, R. C. ; Shankar, P. ( June 2020 , ASEE Annual Conference proceedings)

   The emerging convergence research emphasizes integrating knowledge, methods, and expertise from different disciplines and forming novel frameworks to catalyze scientific discovery and innovation, not only multidisciplinary, but interdisciplinary and further transdisciplinary. Mechatronics matches this new trend of convergence engineering research for deep integration across disciplines such as mechanics, electronics, control theory, robotics, and production manufacturing, and is also inspired by its active means of addressing a specific challenge or opportunity for societal needs. The most current applications of mechatronics in automotive are e-mobility (electric vehicles, EV) and connected and autonomous vehicles (CAV); in manufacturing are robotics and smart-factory; and in aerospace are drones, unmanned aerial vehicle (UAV), and advanced avionics. The growing mechatronics industries demand high quality workforces with multidiscipline knowledge and training. These workforces can come from the graduates of colleges and universities with updated curricula, or from labors returning to schools or taking new training programs. Graduate schools can prepare higher level workforces that can carry out fundamental research and explore new technologies in mechatronics. K-12 schools will also play an important role in fostering the next-decade workforces for all the STEM area. On the other hand, the development of mechatronics technologies improves the tools for teaching mechatronics as well. These new teaching tools include affordable microcontrollers and the peripherals such as Arduinos, and Raspberry Pi, desktop 3D printers, and virtual reality (VR). In this paper we present the working processes and activities of a current one-year ECR project funded by NSF organizing two workshops held by two institutes for improving workforce development environments specified in mechatronics. Each workshop is planned to be two days, where the first day will be dedicated to the topics of the current workforce situation in industry, the current pathways for workforces, conventional college and university workforce training, and K-12 STEM education preparation in mechatronics. The topics in the second day will be slightly different based on the expertise and locations of the two institutes. One will focus on the mechatronics technologies in production engineering for alternative energy and ground mobility, and the other will concentrate on aerospace, alternative energy, and the corresponding applications. Both workshops will also address the current technical development of teaching methods and tools for mechatronics. VR will be specially emphasized and demonstrated in the workshops if the facilities allow. Social impacts of mechatronics technology, expansion of diversity and participation of underrepresented groups will be discussed in the workshops. We expect to have the results of the workshops to present in the annual ASEE conference in June. 

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4. Online Probabilistic Collision Detection for Urban Air Mobility under Data-Driven Uncertainty

   https://doi.org/10.2514/6.2023-2539  

   Wu, Pengcheng ; Chen, Jun ( January 2023 , American Institute of Aeronautics and Astronautics)

   Urban air mobility (UAM) using unmanned aerial vehicles (UAV) is an emerging way of air transportation within metropolitan areas. For the sake of the successful operations of UAM in dynamic and uncertain airspace environments, it is important to provide safe path planning for UAVs. To achieve the path planning with safety assurance, the first step is to detect collisions. Due to uncertainty, especially data-driven uncertainty, it’s impossible to decide deterministically whether a collision occurs between a pair of UAVs. Instead, we are going to evaluate the probability of collision online in this paper for any general data-driven distribution. A sampling method based on kernel density estimator (KDE) is introduced to approximate the data-driven distribution of the uncertainty, and then the probability of collision can be converted to the Riemann sum of KDE values over the domain of the combined safety range. Comprehensive numerical simulations demonstrate the feasibility and eciency of the online evaluation of probabilistic collision for UAM using the proposed algorithm of collision detection. 

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5. Breaking and Fixing Virtual Channels: Domino Attack and Donner

   Aumayr, Lukas ; Moreno-Sanchez, Pedro ; Kate, Aniket ; Maffei, Matteo. ( January 2023 , Network and Distributed System Security (NDSS) Symposium)

   Payment channel networks (PCNs) mitigate the scalability issues of current decentralized cryptocurrencies. They allow for arbitrarily many payments between users connected through a path of intermediate payment channels, while requiring interacting with the blockchain only to open and close the channels. Unfortunately, PCNs are (i) tailored to payments, excluding more complex smart contract functionalities, such as the oracle-enabling Discreet Log Contracts and (ii) their need for active participation from intermediaries may make payments unreliable, slower, expensive, and privacy-invasive. Virtual channels are among the most promising techniques to mitigate these issues, allowing two endpoints of a path to create a direct channel over the intermediaries without any interaction with the blockchain. After such a virtual channel is constructed, (i) the endpoints can use this direct channel for applications other than payments and (ii) the intermediaries are no longer involved in updates. In this work, we first introduce the Domino attack, a new DoS/griefing style attack that leverages virtual channels to destruct the PCN itself and is inherent to the design adopted by the existing Bitcoin-compatible virtual channels. We then demonstrate its severity by a quantitative analysis on a snapshot of the Lightning Network (LN), the most widely deployed PCN at present. We finally discuss other serious drawbacks of existing virtual channel designs, such as the support for only a single intermediary, a latency and blockchain overhead linear in the path length, or a non-constant storage overhead per user. We then present Donner, the first virtual channel construction that overcomes the shortcomings above, by relying on a novel design paradigm. We formally define and prove security and privacy properties in the Universal Composability framework. Our evaluation shows that Donner is efficient, reduces the on-chain number of transactions for disputes from linear in the path length to a single one, which is the key to prevent Domino attacks, and reduces the storage overhead from logarithmic in the path length to constant. Donner is Bitcoin-compatible and can be easily integrated in the LN. 

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